Abstract: Computed tomography (CT) technology has demonstrated significant application value in industrial inspection owing to its non-destructive testing capabilities, high resolution, and visualization features. However, in certain industrial inspection scenarios, extremely limited scanning conditions pose substantial challenges for projection data acquisition, restricting the application of traditional reconstruction methods. To address this challenge, this study proposes an orthogonal dual-view 3D reconstruction network tailored for rapid CT imaging. The proposed method employs an encoder–decoder architecture, utilizing 2D convolutions instead of 3D convolutions to infer the depth dimension of CT volumes through feature channels, thereby enhancing model inference speed. Additionally, gradient information and gradient loss are introduced to strengthen the edge recovery capability of the network. The method is validated on walnut and Fuze datasets. Experimental results showed that reconstructing a volume with a resolution of 128 required only 0.19 s, and the structural similarity of the reconstructed images was higher than 0.98. This approach demonstrates effective capability in inferring 3D CT volumes from dual-view 2D projections, revealing its future potential in rapid CT imaging.